| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 7 | | tagDensity | 0.429 | | leniency | 0.857 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.23% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 570 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 38.60% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 570 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "flickered" | | 1 | "footsteps" | | 2 | "echoed" | | 3 | "gloom" | | 4 | "pulse" | | 5 | "flicked" | | 6 | "weight" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 52 | | matches | (empty) | |
| 87.91% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 52 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 57 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 562 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 62.79% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 516 | | uniqueNames | 14 | | maxNameDensity | 1.74 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 9 | | Raven | 1 | | Nest | 1 | | London | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Saint | 1 | | Christopher | 1 | | Spanish | 1 | | Tomás | 2 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Market" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Tomás" | | 7 | "Morris" |
| | places | | | globalScore | 0.628 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 34 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 562 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 57 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 22 | | mean | 25.55 | | std | 18.26 | | cv | 0.715 | | sampleLengths | | 0 | 65 | | 1 | 3 | | 2 | 55 | | 3 | 56 | | 4 | 30 | | 5 | 20 | | 6 | 48 | | 7 | 23 | | 8 | 41 | | 9 | 5 | | 10 | 29 | | 11 | 17 | | 12 | 9 | | 13 | 46 | | 14 | 15 | | 15 | 24 | | 16 | 25 | | 17 | 6 | | 18 | 5 | | 19 | 22 | | 20 | 9 | | 21 | 9 |
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| 98.52% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 52 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 86 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 57 | | ratio | 0.105 | | matches | | 0 | "She'd been staking out The Raven's Nest for hours, waiting for this bastard—one of Silas' runners—to make a move." | | 1 | "She shouldered through after him, into a narrow stairwell reeking of mildew and something sharper—ozone, maybe, or old magic." | | 2 | "She spun, ready to strike—but it was just a man." | | 3 | "His accent curled around the edges—Spanish, maybe." | | 4 | "The man—Tomás, his name tag said—didn't flinch." | | 5 | "But the weight of the market pressed in around her—too many eyes, too many teeth in the dark." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 133 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 1 | | adverbRatio | 0.007518796992481203 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.007518796992481203 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 57 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 57 | | mean | 9.86 | | std | 6.62 | | cv | 0.672 | | sampleLengths | | 0 | 20 | | 1 | 22 | | 2 | 23 | | 3 | 3 | | 4 | 6 | | 5 | 11 | | 6 | 7 | | 7 | 19 | | 8 | 12 | | 9 | 15 | | 10 | 15 | | 11 | 15 | | 12 | 11 | | 13 | 3 | | 14 | 19 | | 15 | 8 | | 16 | 14 | | 17 | 4 | | 18 | 2 | | 19 | 15 | | 20 | 13 | | 21 | 17 | | 22 | 3 | | 23 | 7 | | 24 | 12 | | 25 | 4 | | 26 | 18 | | 27 | 23 | | 28 | 5 | | 29 | 10 | | 30 | 10 | | 31 | 9 | | 32 | 10 | | 33 | 7 | | 34 | 5 | | 35 | 4 | | 36 | 7 | | 37 | 26 | | 38 | 13 | | 39 | 13 | | 40 | 1 | | 41 | 1 | | 42 | 4 | | 43 | 2 | | 44 | 18 | | 45 | 13 | | 46 | 12 | | 47 | 5 | | 48 | 1 | | 49 | 3 |
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| 73.68% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.47368421052631576 | | totalSentences | 57 | | uniqueOpeners | 27 | |
| 70.92% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 47 | | matches | | 0 | "Just turned on her heel" |
| | ratio | 0.021 | |
| 83.83% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 16 | | totalSentences | 47 | | matches | | 0 | "Her worn leather watch slapped" | | 1 | "He darted around a corner," | | 2 | "She'd been staking out The" | | 3 | "She closed the gap as" | | 4 | "She shouldered through after him," | | 5 | "Her hand went to her" | | 6 | "She didn't draw it." | | 7 | "Her pulse kicked up." | | 8 | "She spun, ready to strike—but" | | 9 | "He wore a Saint Christopher" | | 10 | "he said, voice low" | | 11 | "His accent curled around the" | | 12 | "He nodded toward the bone" | | 13 | "She could push forward." | | 14 | "He'd ended up in a" | | 15 | "She didn't thank him." |
| | ratio | 0.34 | |
| 34.47% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 40 | | totalSentences | 47 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn sprinted after" | | 2 | "Her worn leather watch slapped" | | 3 | "The suspect didn't even glance" | | 4 | "He darted around a corner," | | 5 | "Quinn cursed under her breath," | | 6 | "She'd been staking out The" | | 7 | "The green neon sign of" | | 8 | "The runner was fast, but" | | 9 | "She closed the gap as" | | 10 | "The runner yanked open a" | | 11 | "Quinn didn't hesitate." | | 12 | "She shouldered through after him," | | 13 | "The runner's footsteps echoed below," | | 14 | "Her hand went to her" | | 15 | "She didn't draw it." | | 16 | "The stairs spilled out into" | | 17 | "The air hummed with low" | | 18 | "Stalls lined the platform, their" | | 19 | "The Veil Market." |
| | ratio | 0.851 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 47 | | matches | | 0 | "Now he was leading her" |
| | ratio | 0.021 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 24 | | technicalSentenceCount | 1 | | matches | | 0 | "The runner weaved through the crowd, slipping past a vendor hawking vials of something that glowed faintly blue." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 1 | | matches | | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 7 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 0 | | effectiveRatio | 0 | |